Transfer Learning Action Models by Measuring the Similarity of Different Domains
نویسندگان
چکیده
AI planning requires action models to be given in advance. However, it is both time consuming and tedious for a human to encode the action models by hand using a formal language such as PDDL, as a result, learning action models is important for AI planning. On the other hand, the data being used to learn action models are often limited in planning domains, which makes the learning task very difficult. In this paper, we present a new algorithm to learn action models from plan traces by transferring useful information from other domains whose action models are already known. We present a method of building a metric to measure the shared information and transfer this information according to this metric. The larger the metric is, the bigger the information is transferred. In the experiment result, we show that our proposed algorithm is effective.
منابع مشابه
سنجش سطح قابلیتهای یادگیری سازمانی در بیمارستانها
In the organizational studies, the measurement of organizational learning capabilities has become an increasingly important area. There are several models in literature that have been generated by statistical data from manufacturing firms. In this paper we have used a structural equation model for measurement of organizational learning in hospitals as services firms. In our model, there are fou...
متن کاملImage alignment via kernelized feature learning
Machine learning is an application of artificial intelligence that is able to automatically learn and improve from experience without being explicitly programmed. The primary assumption for most of the machine learning algorithms is that the training set (source domain) and the test set (target domain) follow from the same probability distribution. However, in most of the real-world application...
متن کاملImage Classification via Sparse Representation and Subspace Alignment
Image representation is a crucial problem in image processing where there exist many low-level representations of image, i.e., SIFT, HOG and so on. But there is a missing link across low-level and high-level semantic representations. In fact, traditional machine learning approaches, e.g., non-negative matrix factorization, sparse representation and principle component analysis are employed to d...
متن کاملHierarchical Functional Concepts for Knowledge Transfer among Reinforcement Learning Agents
This article introduces the notions of functional space and concept as a way of knowledge representation and abstraction for Reinforcement Learning agents. These definitions are used as a tool of knowledge transfer among agents. The agents are assumed to be heterogeneous; they have different state spaces but share a same dynamic, reward and action space. In other words, the agents are assumed t...
متن کاملTransfer Learning by Structural Analogy
Transfer learning allows knowledge to be extracted from auxiliary domains and be used to enhance learning in a target domain. For transfer learning to be successful, it is critical to find the similarity between auxiliary and target domains, even when such mappings are not obvious. In this paper, we present a novel algorithm for finding the structural similarity between two domains, to enable t...
متن کامل